14989 results for "cancer studies using rna-seq"

in 30.06 milliseconds.

• RNA sequencing of pediatric BCP ALL

  RNA sequencing data of 33 B-cell precursor (BCP) acute lymphoblastic leukemia (ALL) cases were constructed on an Illumina nextseq 2000 Sequencing System.

  Dataset EGAD50000001125

• Multi-omics characterisation of immune cells in Long Covid

  We generated 10X Multiomics (paired RNA+ATAC) for Long Covid patients with up to 14 months follow-up.

  Study EGAS50000000142

• Strand-specific RNA Sequencing of initial and recurrent gliomas

  Four RNA-sequencing datasets from two patients with initial low-grade glioma and copy number alteration at IDH1 upon recurrence. Data is provided as bam files.

  Dataset EGAD00001003764

• Poikiloderma syndrome RNAseq

  We will sequence the RNA of lymphoblast samples, transformed with EBV, which have poikiloderma syndrome with mutations in c16orf57. The aim of the experiment is to characterise RNA structural effects in this disease.

  Dataset EGAD00001000324

• The Extracellular RNA Quality Control (exRNAQC) study (phase 2)

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 2 (interaction study)

  Dataset EGAD00001009724

• Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX).

  Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX). Available files are raw sequencing fastq files.

  Dataset EGAD00001010102

• Strand-specific RNA sequencing of 16 pairs of primary and relapsed IDH-wt glioblastomas

  This dataset contains strand-specific RNA sequencing data from 16 primary/relapsed sample pairs of IDH-wt glioblastomas

  Dataset EGAD00001004564

• Human serum small non-coding RNA sequencing

  Human small non-coding RNA sequencing of serum from sons of PCOS mothers (n=9) and sons of control mothers (n=9), see publication for details.

  Dataset EGAD00001010165

• RNA sequencing of paediatric glioblastoma in the ICGC PedBrain project

  RNA sequencing of paediatric glioblastoma in the ICGC PedBrain project

  Dataset EGAD00001001944

• RNA sequencing of AML with 3q26 rearrangements

  RNA sequencing of 44 AML cases with diverse 3q26 rearrangements. This dataset is complementary to EGAD00001000726 (which only contains t(3;3)/inv(3)) and to EGAD00001006123.

  Dataset EGAD00001015629

• Mapping Cells and Interactions in the Thymus Across Development and Aging (2025-07-28)

  Single cell RNA sequencing on thymic cells from children with syndromic diseases such as Trisomy 21 and inflammatory/autoimmune diseases such as myasthenia gravis. Our group has previously performed atlasing of the healthy human thymus using single cell transcriptomics and obtained an unprecedented understanding of human thymus throughout development. In this study, we intend to survey diseased human thymus and compare this to healthy to obtain a better understanding of thymic dysfunction in human diseases. The diseases we will survey include congenital conditions such as Down's syndrome, CHARGE syndrome, DiGeorge syndrome as well as acquired diseases such as myasthenia gravis and thymic hyperplasia. We intend to perform genetic and genomic study on the samples, including expression analysis. This will not only help us to understand the cells and molecular pathways affected in dysfunctional thymus; but may also lead to generalisable understandings in human genetic diseases and inflammatory/autoimmune diseases. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2025-07-28.

  Dataset EGAD00001015660

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma WES

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014787

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyosarcoma WES

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014789

• TRACERx NSCLC, multiregion sequencing of the first 100 tumors

  BACKGROUND TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). METHODS Multiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTS Widespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis. CONCLUSIONS ITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

  Dataset EGAD00001003206

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Sequencing data for oesophageal and related samples - Ganguli et al (RNA)

  Data supporting: "TBC" Ganguli et al (RNA for 394 samples)

  Dataset EGAD00001011190

• The genomic complexity of early T-cell progenitor acute lymphoblastic leukemia

  CREST The accurate identification of structural variations using whole-genome DNA sequencing data generated by next-generation sequencing technology is extremely difficult. To address this challenge, we have developed CREST, an algorithm that uses sequencing reads with partial alignments to the reference human genome (so-called soft-clipped reads) to directly map the breakpoints of somatic structural variations. We applied CREST to paired tumor/normal whole genome sequencing data from five cases of T-lineage acute lymphoblastic leukemia (T-ALL). A total of 110 somatic structural variants were identified, >80% of which were validated by genomic PCR and Sanger sequencing. The validated structural variants included 31 inter-chromosomal translocations, 19 intra-chromosomal translocations, one inversion, 22 deletions and 16 insertions. A comparison of the results generated with CREST to those obtained using the traditional paired-end discordant mapping methods demonstrate CREST to have a much higher sensitivity and specificity. In addition, application of CREST to publicly available whole-genome sequencing data from the human melanoma cancer cell line COLO-829 demonstrated the identification of 50 novel structural variations not detected using the standard methods, 20 of which were selected for validation with a 90% success rate. These data demonstrate that direct mapping of soft-clipped reads offers an improved method for detecting structural variants at the nucleotide level of resolution. T-ALL Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.

  Study phs000340

• Germline whole genome sequencing of patients with Li-Fraumeni syndrome

  Five hundred ng to 1 ug of genomic DNA was submitted to The Centre for Applied Genomics (TCAG) at The Hospital for Sick Children for genomic library preparation and whole genome sequencing. DNA samples were quantified using the Qubit High Sensitivity Assay and purity was assessed using the Nanodrop OD 260/280 ratio. Approximately 500-700 ng of DNA was used as input material for library preparation using the Illumina TruSeq PCR-free DNA Library Prep Kit following the manufacturer’s recommended protocol. In brief, DNA was fragmented to 400 bp on average using sonication on a Covaris LE220 instrument. Fragmented DNA was end-repaired, A-tailed and indexed TruSeq Illumina adapters with overhang-T were added to the DNA. Libraries were then validated on a Bioanalyzer DNA High Sensitivity chip to check for size and absence of primer dimers and quantified by qPCR using Kapa Library Quantification Illumina/ABI Prism Kit protocol (KAPA Biosystems). Validated libraries were pooled in equimolar quantities and paired-end sequenced on an Illumina HiSeq X platform following Illumina’s recommended protocol to generate paired-end reads of 150-bases in length and an average depth of 40X. FASTQ files were aligned to the hg19 reference genome using BWA-mem v0.78. PCR duplicates were marked with Picard MarkDuplicates v1.1.08, and base recalibration and realignment was performed using GATK v2.8.1.

  Study EGAS00001007061

• Chromatin Landscape of BET Inhibitor-Treated CD8+ T-cells from Chronic Lymphocytic Leukemia Patients

  The pan-BET (bromodomain and extra-terminal) inhibitor, OPN-51107 (OPN5) was found to alleviate features of T-cell exhaustion in models of chronic lymphocytic leukemia (CLL). To evaluate if OPN5 treatment could relieve fixed chromatin states associated with terminal T-cell dysfunction, ATAC-sequencing was performed on CLL patient T-cells. Peripheral blood mononuclear cells (PBMCs) from 4 treatment-naïve CLL patients were treated ex vivo with 0.5 μM OPN5 or equivalent vehicle (DMSO) in the presence of T-cell activating stimuli (10 μg/mL anti-CD3 and 5 μg/mL anti-CD28) for 16 hours. Live CD8+ T-cells were then isolated via fluorescence-activated cell sorting, and approximately 100,000 viable CD8+ T-cells were subjected to ATAC-sequencing. Libraries were prepared using the Active Motif ATAC-seq kit and underwent paired-end sequencing (2 x 50 bp, 100 cycles) on an Illumina NovaSeq 6000 SP sequencer. Analysis of this data revealed that OPN5 treatment induces a unique chromatin landscape in CLL patient T-cells that is less like that of terminally exhausted T-cells and more like that of effector and memory T-cells. In particular, OPN5 treatment reduced chromatin accessibility at promoter regions of terminal exhaustion-associated genes (e.g., CD101, CCR5, LAG3, BATF3) and increased chromatin accessibility at promoter regions of genes associated with progenitor T-cell function (e.g., TCF7, IL2, CXCR5, IFNG). Individual raw sequencing (fastq) files and processed bigwig files (aligned to the hg38 human reference genome), as well as bigwig files for grouped OPN5- and vehicle-treated samples (aggregate of all 4 patient samples per treatment group) are available via authorized access request through dbGaP.

  Study phs003613

• Dataset for WGS and RNA melanoma samples

  This dataset contains WGS and RNA sequencing data for 4 melanoma samples. Sequencing was performed on Illumina Novaseq 6000 and Illumina HiSeq X. The sequencing was always paired.

  Dataset EGAD50000000093

• B cell single cell sequencing (RNA + VDJ) - Autoproliferation

  10X 3' (RNA) and 10X 5' (VDJ) samples of B cells (non-proliferating/hi and autoproliferating/Dim) for healthy donor and Multiple sclerosis patients used in the paper.

  Dataset EGAD50000001237

• Hematological toxicity following CAR-T cells injection dataset

  This dataset containes 44 paired of bulk RNA sequencing and 42 parired of single cell RNA sequencing issued from bone marrow aspirations obtained from patients treated with CAR-T (CD3+, CAR T+, CD34+ sorted populations).

  Dataset EGAD50000001142

• Integrated Multiomics Uncovers Distinct Macrophage Alterations in Human Metabolic dysfunction-Associated Steatohepatitis Progression

  We report single-nuclei RNA sequencing data from human livers in metabolic dysfunction-associated steatotic liver disease (MASLD) patients.

  Study EGAS50000000768

• Vascularized human retinal organoids

  Single cell RNA sequencing of human retinal organoids that have been vascularized. Assesses the impact of vascularization on cell types and their gene expression.

  Study EGAS50000000828

• RNA sequencing of adult T-cell leukemia/lymphoma

  RNA sequencing of 57 tumor samples of adult T-cell leukemia/lymphoma as well as 3 samples of HTLV-1 carrier and 3 samples of healthy volunteers.

  Dataset EGAD00001001411

• Clear cell sarcoma dataset

  This deposit consists of DNA and RNA sequencing data from 67 CCS samples. 55 samples had tumor DNA sequencing data. 6 had matched normal sequencing data. 34 samples had tumor RNA sequencing data.

  Dataset EGAD00001008611

• RNA sequencing upon depletion of RAF kinases vs. MAPK inhibition in KRAS mutant cells

  RNA Sequencing upon shRNA mediated depletion of RAF kinases or treatment with Cobimetinib (GDC-0973, 250nM, 6hrs) or with pan RAFi (AZ-628, 10uM, 6hrs)

  Dataset EGAD00001008384

• RNA data of neuroblastoma patients

  94 sample with multi-omics analysis of ALT-positive neuroblastoma tumors, rna sequencing

  Dataset EGAD00001006587

• Single-cell RNA-sequencing of nivolumab-treated glioblastoma

  Single-cell RNA-sequencing of 8 patients, from primary and relapse tumour (total of 16 samples). Patients were treated with nivolumab prior to relapse surgery.

  Dataset EGAD00001010843

• Bulk RNA-sequencing of nivolumab-treated glioblastoma

  Bulk RNA-sequencing of 30 relapse tumour samples. 20 patients were treated with nivolumab prior to relapse surgery, while 10 patients are control patients.

  Dataset EGAD00001010844